Carol Cuesta-Lazaro, Massachusetts Institute of Technology
When
TAP Cosmology Initiative Lecture
Carol Cuesta-Lazaro, Massachusetts Institute of Technology
Refreshments served at 3:00pm in the 3rd floor atrium
Title: Beyond the Observable: A Machine Learning perspective on modern Cosmology
Abstract: Our observations have painted a simple portrait of cosmic evolution, yet fundamental questions remain unanswered: What is the nature of dark matter, the invisible substance that makes up most of the matter in the Universe? What is driving the accelerated expansion of the cosmos? How did it begin? Addressing these questions demands integrating advances in machine learning, high-performance computing, and astrophysics. In this talk, I will present frameworks that bridge the gap between numerical simulations and increasingly precise astronomical observations to decode these invisible components. I will demonstrate how generative models can analyze astronomical data at its full complexity, while remaining robust to uncertainties in galaxy formation physics. This allows us not only to reconstruct the dark matter distribution and primordial Universe from observed galaxy clustering, but also to identify potential anomalies that might signal new physics beyond our standard cosmological model. These advances come at a pivotal moment, as we enter an era of extremely precise cosmological surveys that may transform current statistical tensions into discoveries of new fundamental physics.
Bio: Carol is an IAIFI Fellow at the NSF Institute for Artificial Intelligence and Fundamental Interactions. She splits her time between MIT and the Center for Astrophysics at Harvard, developing robust machine learning models that can guide us towards future discoveries in physics. Carolina received her Ph.D. in Physics and Data Intensive Science from the Institute of Computational Cosmology at Durham University, UK, in 2022. Alongside her PhD, she has been a research collaborator with the United Nations (UN) Global Pulse and the UK’s National Health Service (NHS), developing epidemiological simulations, and a research intern at Amazon’s Alexa team. She is also passionate about science communication and making astrobabble understandable.